
# Libraries for Basic Process 
import numpy as np
import pandas as pd

# Libraries for Visualization
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline 

# Pre-setting Plot Style
font={'size':15}
plt.rc('font', **font)
plt.rc('xtick',labelsize=12)    
plt.rc('ytick',labelsize=12)
sns.set_style({'xtick.bottom':True,'ytick.left':True,'text.color':'#9400D3',
               'axes.labelcolor': 'blue','patch.edgecolor': 'black'})

# sklearn Modules
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier

# Supporting Modules
from scipy.stats import zscore
from imblearn.over_sampling import SMOTE

# Module to Suppress Warnings
from warnings import filterwarnings
filterwarnings('ignore')


# Loading the files and creating dataframes
Normal = pd.read_csv('Normal.csv')
Type_H = pd.read_csv('Type_H.csv')
Type_S = pd.read_csv('Type_S.csv')

# Getting Shape and Size of each data
N = Normal.shape
H = Type_H.shape
S = Type_S.shape

# Displaying Normal Dataset
print('\033[1m1. Normal Dataset consist:-\033[0m\n Number of Rows    =',N[0],'\n Number of Columns =',N[1])
display(Normal.head())
print('_________________________________________________________________________________')

# Displaying Type_H Dataset
print('\033[1m\n2. Type_H Dataset consist:-\033[0m\n Number of Rows    =',H[0],'\n Number of Columns =',H[1])
display(Type_H.head())
print('_________________________________________________________________________________')

# Displaying Type_S Dataset
print('\033[1m\n3. Type_S Dataset consist:-\033[0m\n Number of Rows    =',S[0],'\n Number of Columns =',S[1])
display(Type_S.head())

1. Normal Dataset consist:-
 Number of Rows    = 100 
 Number of Columns = 7

_________________________________________________________________________________

2. Type_H Dataset consist:-
 Number of Rows    = 60 
 Number of Columns = 7

_________________________________________________________________________________

3. Type_S Dataset consist:-
 Number of Rows    = 150 
 Number of Columns = 7


# Merging all three datasets into single dataset
biodata = Normal.append([Type_H,Type_S])

# Getting Shape and Size of final dataset
B = biodata.shape

# Displaying Final Dataset
print('\033[1mFinal Dataset consist:-\033[0m\n Number of Rows    =',B[0],'\n Number of Columns =',B[1])
display(biodata.head(10))

Final Dataset consist:-
 Number of Rows    = 310 
 Number of Columns = 7


# Exploring dataset
print('\n\033[1mData Types of Each Attribute:-')
display(biodata.dtypes.to_frame('Data Type'))

Data Types of Each Attribute:-


# Checking for Null Values
print('\n\033[1mNull Values in the Attributes:-')
display(biodata.isnull().sum().to_frame('Null Values'))

Null Values in the Attributes:-


# Checking unique values of Class Attribute
print('\n\033[1mTotal Unique Values of Attribute Class =',biodata.Class.nunique())
print('\n\033[1mUnique Values are:-')
display(biodata.groupby('Class').size().to_frame('Total Values'))

Total Unique Values of Attribute Class = 6

Unique Values are:-


# Replacing incorrect unique values with correct values
biodata['Class']=biodata['Class'].replace({'Nrmal':'Normal','type_h':'Type_H','tp_s':'Type_S'},regex=True)

# Checking unique values after replacement
print('\n\033[1mTotal Unique Values After Replacement =',biodata.Class.nunique())
print('\n\033[1mUnique Values are:-')
display(biodata.groupby('Class').size().to_frame('Total Values'))

Total Unique Values After Replacement = 3

Unique Values are:-


# Describing the data interms of count, mean, standard deviation, and 5 point summary
print('\n\033[1mBrief Summary of Dataset:-')
display(biodata.describe())

Brief Summary of Dataset:-


# Summary Based on Dependent Variable Class
print('\n\033[1mSummary Based on Dependent Variable:-')
display(biodata.groupby('Class').describe().T)

Summary Based on Dependent Variable:-


# Checking skewness of the data attributes
print('\033[1mSkewness of all attributes:-')  
display(biodata.skew().to_frame(name='Skewness'))

Skewness of all attributes:-


# Getting Interquartile Range of data attributes
print('\033[1mIQR of all attributes:-')
display((biodata.quantile(0.75) - biodata.quantile(0.25)).to_frame(name='Interquartile Range'))

IQR of all attributes:-


# Checking Covariance related with all independent attributes
print('\033[1mCovariance between all attributes:-')
display(biodata.cov())

Covariance between all attributes:-


# Checking Correlation by plotting Heatmap for all independent attributes 
print('\033[1mHeatmap showing Correlation of Data attributes:-')
plt.figure(figsize=(10,8))
plt.title('Correlation of Data Attributes\n')
sns.heatmap(biodata.corr(),annot=True,fmt= '.2f',cmap='magma');
plt.show()

Heatmap showing Correlation of Data attributes:-


# Information about P_incidence
print('\033[1mTable showing information about P_incidence:-')
display(biodata['P_incidence'].describe().to_frame())

# Plotting Distribution for attribute P_incidence
print('\033[1m\nPlot Showing Distribution of Attribute P_incidence:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of P_incidence\n')
sns.distplot(biodata['P_incidence'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plotting for 5 point summary with outliers of Attribute P_incidence
print('\033[1mPlot Showing 5 point summary with outliers of Attribute P_incidence:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for P_incidence\n')
sns.boxplot(biodata['P_incidence'],color="#9400D3");
plt.show()

Table showing information about P_incidence:-


Plot Showing Distribution of Attribute P_incidence:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute P_incidence:-


# Information about P_tilt
print('\033[1mTable showing information about P_tilt:-')
display(biodata['P_tilt'].describe().to_frame())

# Plotting Distribution for attribute P_tilt
print('\033[1m\nPlot Showing Distribution of Attribute P_tilt:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of P_tilt\n')
sns.distplot(biodata['P_tilt'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plot for 5 point summary with outliers of Attribute P_tilt
print('\033[1mPlot Showing 5 point summary with outliers of Attribute P_tilt:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for P_tilt\n')
sns.boxplot(biodata['P_tilt'],color="#9400D3");
plt.show()

Table showing information about P_tilt:-


Plot Showing Distribution of Attribute P_tilt:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute P_tilt:-


# Information about L_angle
print('\033[1mTable showing information about L_angle:-')
display(biodata['L_angle'].describe().to_frame())

# Plotting Distribution for attribute L_angle
print('\033[1m\nPlot Showing Distribution of Attribute L_angle:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of L_angle\n')
sns.distplot(biodata['L_angle'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plot for 5 point summary with outliers of Attribute L_angle
print('\033[1mPlot Showing 5 point summary with outliers of Attribute L_angle:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for L_angle\n')
sns.boxplot(biodata['L_angle'],color="#9400D3");
plt.show()

Table showing information about L_angle:-


Plot Showing Distribution of Attribute L_angle:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute L_angle:-


# Information about S_slope
print('\033[1mTable showing information about S_slope:-')
display(biodata['S_slope'].describe().to_frame())

# Plotting Distribution for attribute S_slope
print('\033[1m\nPlot Showing Distribution of Attribute S_slope:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of S_slope\n')
sns.distplot(biodata['S_slope'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plot for 5 point summary with outliers of Attribute S_slope
print('\033[1mPlot Showing 5 point summary with outliers of Attribute S_slope:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for S_slope\n')
sns.boxplot(biodata['S_slope'],color="#9400D3");
plt.show()

Table showing information about S_slope:-


Plot Showing Distribution of Attribute S_slope:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute S_slope:-


# Information about P_radius
print('\033[1mTable showing information about P_radius:-')
display(biodata['P_radius'].describe().to_frame())

# Plotting Distribution for attribute P_radius
print('\033[1m\nPlot Showing Distribution of Attribute P_radius:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of P_radius\n')
sns.distplot(biodata['P_radius'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plot for 5 point summary with outliers of Attribute P_radius
print('\033[1mPlot Showing 5 point summary with outliers of Attribute P_radius:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for P_radius\n')
sns.boxplot(biodata['P_radius'],color="#9400D3");
plt.show()

Table showing information about P_radius:-


Plot Showing Distribution of Attribute P_radius:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute P_radius:-


# Information about S_Degree
print('\033[1mTable showing information about S_Degree:-')
display(biodata['S_Degree'].describe().to_frame())

# Plotting Distribution for attribute S_Degree
print('\033[1m\nPlot Showing Distribution of Attribute S_Degree:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of S_Degree\n')
sns.distplot(biodata['S_Degree'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

# Plot for 5 point summary with outliers of Attribute S_Degree
print('\033[1mPlot Showing 5 point summary with outliers of Attribute S_Degree:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for S_Degree\n')
sns.boxplot(biodata['S_Degree'],color="#9400D3");
plt.show()

Table showing information about S_Degree:-


Plot Showing Distribution of Attribute S_Degree:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute S_Degree:-


# Information about Class
print('\033[1mTable showing information about Class:-')
display(biodata['Class'].describe().to_frame())

# Plotting Frequency Distribution for attribute Class
colors = ['gold', 'yellowgreen', 'tomato']
print('\033[1m\nPlot Showing Frequency Distribution of Attribute Class:-')
plt.figure(figsize=(10,8))
plt.title('Frequencies of Class Attribute\n')
sns.countplot(biodata['Class'],palette='bright');
plt.show()
print('___________________________________________________________________________________')
print('')

# Plotting Pie Chart to check contribution
print('\033[1mPie Chart Showing Contribution of Each Category of Attribute Class:-')
plt.title('Contribution of Each Category of Class Attribute\n\n\n\n\n\n')
explode = (0.05, 0.1, 0.1)
biodata['Class'].value_counts().plot.pie(radius=2.5,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-90,colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2, 1))
plt.show()

Table showing information about Class:-


Plot Showing Frequency Distribution of Attribute Class:-

___________________________________________________________________________________

Pie Chart Showing Contribution of Each Category of Attribute Class:-


# Bivariate Analysis of Class VS P_incidence Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS P_incidence:-')
plt.figure(figsize=(12,6))
plt.title('Class VS P_incidence\n')
sns.violinplot(x='Class', y='P_incidence', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS P_incidence:-


# Bivariate Analysis of Class VS P_tilt Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS P_tilt:-')
plt.figure(figsize=(12,6))
plt.title('Class VS P_tilt\n')
sns.violinplot(x='Class', y='P_tilt', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS P_tilt:-


# Bivariate Analysis of Class VS L_angle Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS L_angle:-')
plt.figure(figsize=(12,6))
plt.title('Class VS L_angle\n')
sns.violinplot(x='Class', y='L_angle', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS L_angle:-


# Bivariate Analysis of Class VS S_slope Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS S_slope:-')
plt.figure(figsize=(12,6))
plt.title('Class VS S_slope\n')
sns.violinplot(x='Class', y='S_slope', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS S_slope:-


# Bivariate Analysis of Class VS P_radius Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS P_radius:-')
plt.figure(figsize=(12,6))
plt.title('Class VS P_radius\n')
sns.violinplot(x='Class', y='P_radius', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS P_radius:-


# Bivariate Analysis of Class VS S_Degree Attributes
print('\033[1mPlot Showing Bivariate Analysis of Class VS S_Degree:-')
plt.figure(figsize=(12,6))
plt.title('Class VS S_Degree\n')
sns.violinplot(x='Class', y='S_Degree', data=biodata, palette='bright');
plt.show()

Plot Showing Bivariate Analysis of Class VS S_Degree:-


# Multivariate Analysis of Attributes

# Histogram for Independent Attributes
print('\033[1mPlot Showing Counts of Independent Attributes:-')
plt.figure(figsize=(16,8))
plt.xlabel('Attributes')
plt.title('Counts of Independent Attributes')
sns.histplot(biodata,bins=80,palette='colorblind',binwidth=10);
plt.show()

# Pair plot for Independent Attributes
print('\033[1m\nPlot Showing Multivariate Analysis to check Relation between Independent Attributes:-')
sns.pairplot(biodata,plot_kws={'color':'#9400D3'},diag_kws={'color':'Gold'}).fig.suptitle(
    'Relation Between Independent Attributes',y=1.04);
plt.show()

Plot Showing Counts of Independent Attributes:-


Plot Showing Multivariate Analysis to check Relation between Independent Attributes:-


# Multivariate Analysis to check Density of Categorical Attribute
print('\033[1mPlot Showing Multivariate Analysis to check Density of Categorical Attribute:-')
sns.pairplot(biodata,hue='Class',palette='bright').fig.suptitle('Density of Categorical Attribute',y=1.04);
plt.show()

Plot Showing Multivariate Analysis to check Density of Categorical Attribute:-


# Plotting Heatmap for checking Correlation
print('\033[1mHeatmap showing Correlation of Data attributes:-')
plt.figure(figsize=(12,10))
plt.title('Correlation of Data Attributes\n')
sns.heatmap(biodata.corr(),annot=True,fmt= '.2f',cmap='flare');
plt.show()

Heatmap showing Correlation of Data attributes:-


# Getting Outliers and Imputing Outliers by Mean

AT = []
OL1 = []
OL2 = []
M1 = []
M2 = []
for i in biodata.columns:
    if i!='Class':
        AT.append(i)
        
        # Getting Interquartile Range
        q1 = biodata[i].quantile(0.25)
        q3 = biodata[i].quantile(0.75)
        IQR = q3 - q1
        
        # Getting Mean of Attribute having Outliers (i.e including outliers)
        M1.append(round(biodata[i].mean(),2)) 
        
        # Getting Outlier and Normal Values Seperated
        OL = []
        NOL = []
        for k in biodata[i]:
            if (k < (q1 - 1.5 * IQR) or k > (q3 + 1.5 * IQR)):
                OL.append(k)
            else:
                NOL.append(k)
        OL1.append(len(OL))
        
        # Replacing Outliers by Mean of Normal Values
        biodata[i].replace(OL,np.mean(NOL),inplace=True) # Here we are imputing outliers by Mean of attribute without outlier
        M2.append(round(np.mean(NOL),2))
        
        # Getting Outliers After Imputation
        OL_cnt = 0
        for k in biodata[i]:
            if (k < (q1 - 1.5 * IQR) or k > (q3 + 1.5 * IQR)):
                OL_cnt += 1
        OL2.append(OL_cnt)
        
# Creting dataframe for better representation of Outlier Analysis      
Outlier_Analysis = pd.DataFrame({'Attribute':AT,
                                 'Mean Including Outliers':M1,
                                 'Outliers Before Imputation':OL1,
                                 'Mean Excluding Outliers':M2,
                                 'Outliers After Imputation':OL2})
print('\033[1mTotal Outliers Observed in Dataset =',sum(OL1))
print('\n\033[1mTable Showing Outlier Analysis:-')
display(Outlier_Analysis)

Total Outliers Observed in Dataset = 39

Table Showing Outlier Analysis:-


# Converting Target Attribute to Categorical Data Type
biodata['Class'] = biodata['Class'].astype('category')

# Encoding Target Attribute
biodata['Class'] = biodata['Class'].cat.codes

# For Better Observation 
print('\033[1mTable Showing Encoded Target Attributes:-')
Classes = pd.DataFrame({'Class':['Normal','Type_H','Type_S']})
Classes['Encoded Value'] = biodata.groupby('Class').size().index
Classes['Total Observations'] = biodata.groupby('Class').size()
display(Classes)

Table Showing Encoded Target Attributes:-


# Seperating Independent and Dependent Attributes

# Getting Predictors by dropping Class Attribute
X = biodata.drop(columns='Class')

# Getting Target Attribute
y = biodata['Class'] 

# Displaying Segregated Predictors
print('\033[1mTable Showing Segregated Predictors:-')
display(X.head())

Table Showing Segregated Predictors:-


# Applying Z-Scores to Predictors
X_Scale = X.apply(zscore)

# Checking the Mean and Standard Deviation
print('\033[1mTable Showing Mean and Standard Deviation of Scaled Predictors:-')
display(X_Scale.describe()[1:3].T)

Table Showing Mean and Standard Deviation of Scaled Predictors:-


# Checking Value Counts of Target Attribute
print('\033[1mTable Showing Total Observations in each class:-')
print('\033[1m--> Normal = 0\n    Type_H = 1\n    Type_S = 2')
TAC = y.value_counts().to_frame('Total Observations')
display(TAC)

# Getting Percentages of each category in Target Attribute
labels = ['Type_S','Normal','Type_H']
print('\033[1m\n\nPie Chart Showing Percentage of Each Category of Target Attribute:-')
plt.title('Percentage of Each Category of Target Attribute\n\n\n\n\n\n')
explode = (0.05, 0.1, 0.1)
y.value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-90,labels=labels,colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Table Showing Total Observations in each class:-
--> Normal = 0
    Type_H = 1
    Type_S = 2



Pie Chart Showing Percentage of Each Category of Target Attribute:-


# Getting total observations of target attribute before transformation
yct = y.count()

# Transforming the dataset
OS = SMOTE(random_state=1)
X, y = OS.fit_resample(X_Scale, y)

# Checking Value Counts of Target Attribute after transforming
print('\033[1mTable Showing Total Observations in each section of target data for SMOTE:-') 
print('\033[1m--> Normal = 0\n    Type_H = 1\n    Type_S = 2')
TAC2 = y.value_counts().to_frame('Total Observations')

# For better representation of Transformation
TVC = pd.DataFrame({'Before Tranformation':TAC['Total Observations'],'After Tranformation':TAC2['Total Observations']})
total  = pd.Series({'Before Tranformation':yct,'After Tranformation':y.count()},name='Total')
TVC = TVC.append(total)
columns=[('__________Total Observations__________', 'Before Tranformation'), ('__________Total Observations__________', 
                                                                              'After Tranformation')]
TVC.columns = pd.MultiIndex.from_tuples(columns)
display(TVC)

# Getting Percentages of each category in Target Attribute
labels = ['Type_S','Normal','Type_H']
print('\033[1m\n\nPie Chart Showing Percentage of Each Category of Target Attribute:-')
plt.title('Percentage of Each Category of Target Attribute\n\n\n\n\n\n')
explode = (0.05, 0.1, 0.1)
y.value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-90,labels=labels,colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Table Showing Total Observations in each section of target data for SMOTE:-
--> Normal = 0
    Type_H = 1
    Type_S = 2



Pie Chart Showing Percentage of Each Category of Target Attribute:-


# Splitting into Train and Test Sets in 

# Here test_size is not given because by default its value is 0.25.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)

# For better observation of Splitted Data
TTS = pd.DataFrame({'Train':y_train.value_counts(),'Test':y_test.value_counts(),'Total Observations':y.value_counts()})
total  = pd.Series({'Train':y_train.count(),'Test':y_test.count(),'Total Observations':y.shape[0]},name='Total')
TTS = TTS.append(total)
print('\033[1mTable Showing Train-Test Split of Data:-')
display(TTS)

Table Showing Train-Test Split of Data:-


# Building KNN Model
KNN = KNeighborsClassifier(metric = 'euclidean') # By default n_neighbor = 5

# Fitting Nearest Neighbour Algorithm on Train
KNN.fit(X_train, y_train)

# Predicting on the Test set
Prediction = KNN.predict(X_test)


# Finding Accuracies for Train and Test
Train_AC = round(KNN.score(X_train, y_train)*100,2)
Test_AC = round(KNN.score(X_test, y_test)*100,2)

# For better Repesentation of Accuracies for Train and Test Data
print('\033[1mTable Showing Accuracies for Train and Test Data:-')
display(pd.DataFrame({'Data':['Training','Testing'],'Accuracy (%)':[Train_AC,Test_AC]}).set_index('Data'))

Table Showing Accuracies for Train and Test Data:-


# Getting Classification Report
CR = metrics.classification_report(y_test,Prediction,output_dict=True)

# Displaying Classification Report
print('\033[1mTable Showing Classification Report:-')
print('\033[1m--> Normal = 0\n    Type_H = 1\n    Type_S = 2')
display(pd.DataFrame(CR))

Table Showing Classification Report:-
--> Normal = 0
    Type_H = 1
    Type_S = 2


# Building Confusion Matrix
CM = metrics.confusion_matrix(y_test, Prediction)
Con_Mat = pd.DataFrame(CM, index=[i for i in ["Normal","Type_H","Type_S"]], columns=[i for i in ["Pred_Normal",
                                                                                                 "Pred_Type_H","Pred_Type_S"]])

# Displaying Confusion Matrix
print('\033[1m Heatmap Showing Performance of KNN model:-')
plt.figure(figsize = (7,5))
sns.heatmap(Con_Mat, annot=True)
plt.title('Confusion Matrix\n')
plt.xlabel('\nPredicted Labels\n')
plt.ylabel('Actual Labels\n')
plt.show()

 Heatmap Showing Performance of KNN model:-


# Creating odd list of K-Value for KNN
k_val = list(range(1, 50, 2))

# Creating empty lists that will hold Misclassification Errors
Train_Error = []
Test_Error = []

# Getting Misclassification Errors for all the Predicted values
for i in k_val:
    knn = KNeighborsClassifier(n_neighbors=i, metric='euclidean')
    Train_scores = cross_val_score(knn, X_train, y_train, scoring='accuracy',cv=5)
    Test_scores = cross_val_score(knn, X_test, y_test, scoring='accuracy',cv=5)
    Train_Error.append(1-Train_scores.mean())      # 1 - Score = Error
    Test_Error.append(1-Test_scores.mean())

# Displaying Mean Errors for Train and Test data
print('\033[1mTable showing Mean Errors for Train and Test data:-')
display(pd.DataFrame({'Data':['Train','Test'],'Mean Error':[np.mean(Train_Error),np.mean(Test_Error)]}).set_index('Data'))

Table showing Mean Errors for Train and Test data:-


# Plot for Training Data

print('\033[1mPlot Showing Misclassification Errors of Training Data for K-Values:-')
plt.figure(figsize=(12, 6))
plt.title('Misclassification Errors of Training Data VS K-Values\n')
plt.plot(k_val, Train_Error, color='red',markerfacecolor='#9400D3', linestyle='dashed', marker='o', markersize=10)
plt.xlabel('K Value')
plt.ylabel('Error Rate')
plt.show()
print('_____________________________________________________________________________________________________')
print('')

# Plot for Testing Data

print('\033[1mPlot Showing Misclassification Errors of Testing Data for K-Values:-')
plt.figure(figsize=(12, 6))
plt.title('Misclassification Errors of Testing Data VS K-Values\n')
plt.plot(k_val, Test_Error, color='red',markerfacecolor='#9400D3', linestyle='dashed', marker='o', markersize=10)
plt.xlabel('K Value')
plt.ylabel('Error Rate')
plt.show()

Plot Showing Misclassification Errors of Training Data for K-Values:-

_____________________________________________________________________________________________________

Plot Showing Misclassification Errors of Testing Data for K-Values:-


# Creating K-Value List along with empty lists
K_Value = list(range(1,20,2))
train_ac = []
test_ac = []
normal = []
type_h = []
type_s = []

# Getting Accuracies and Precisions for K-Values
for i in K_Value:
    # Build KNN Model
    knn = KNeighborsClassifier(n_neighbors=i, metric='euclidean')
    knn.fit(X_train, y_train)
    # Appending Accuracies of Train and Test Sets
    train_ac.append(round(knn.score(X_train, y_train)*100,2))
    test_ac.append(round(knn.score(X_test, y_test)*100,2))
    # Prediction on Test Set
    pred = knn.predict(X_test)
    # Getting Classification Report
    cr = metrics.classification_report(y_test,pred,output_dict=True)
    normal.append(round(cr['0']['precision'],2))
    type_h.append(round(cr['1']['precision'],2))
    type_s.append(round(cr['2']['precision'],2))

# Creating Dataframe for better Representation
print('\033[1mTable Showing Accuracies and Precisions for K-Values:-')
KVal =pd.DataFrame({'K-Value':K_Value,'Train':train_ac,'Test':test_ac,'Normal':normal,'Type_H':type_h,
                    'Type_S':type_s}).set_index('K-Value')
columns=[('Accuracy (%)', 'Train'), ('Accuracy (%)', 'Test'), ('_______Precision_______', 'Normal'), 
         ('_______Precision_______', 'Type_H'), ('_______Precision_______', 'Type_S')]
KVal.columns = pd.MultiIndex.from_tuples(columns)
display(KVal)

Table Showing Accuracies and Precisions for K-Values:-


# Building KNN Model for K=3 
KNN = KNeighborsClassifier(metric = 'euclidean',n_neighbors = 3) # By default n_neighbor = 5

# Fitting Nearest Neighbour Algorithm on Train
KNN.fit(X_train, y_train)

# Finding Accuracies for Train and Test
Train_AC2 = round(KNN.score(X_train, y_train)*100,2)
Test_AC2 = round(KNN.score(X_test, y_test)*100,2)

# For better Repesentation of Accuracies for Train and Test Data
print('\033[1mTable Showing Accuracies for Train and Test Data:-')
acc = pd.DataFrame({'Data':['Training','Testing'],'K=5 (Default)':[Train_AC,Test_AC],
                      'K=3':[Train_AC2,Test_AC2]}).set_index('Data')
columns=[('___Accuracy (%)___', 'K=5 (Default)'), ('___Accuracy (%)___', 'K=3')]
acc.columns = pd.MultiIndex.from_tuples(columns)
display(acc)

Table Showing Accuracies for Train and Test Data:-


# Creating List of Models
Models = [LogisticRegression(), KNeighborsClassifier(n_neighbors=3, metric='euclidean'), GaussianNB(), SVC(gamma='auto'),
          SVC(kernel='linear',gamma='auto'), RandomForestClassifier(), DecisionTreeClassifier()]

# Getting Accuracies and Error Rates from Models
Model_Accuracy = []
Model_Error = []
for i in Models:
    Model_Accuracy.append(round(cross_val_score(i,X,y,scoring='accuracy',cv=5).mean(),3)*100)
    Model_Error.append(round(1-cross_val_score(i,X,y,scoring='accuracy',cv=5).mean(),3)*100)
    
# Displaying Accuracies and Error Rates from Models
print('\033[1mTable Showing Accuracies and Error Rates from Varius Algorithms:-')
model = ['Logistic Regression','K-Neighbors Classifier','Gaussian Naive Bayes','RBF Support Vector Classifier',
        'Linear Support Vector Classifier','Random Forest Classifier','Decision Tree Classifier']
display(pd.DataFrame({'Trained Model':model,'Accuracy (%)':Model_Accuracy,'Error Rate (%)':Model_Error}))

Table Showing Accuracies and Error Rates from Varius Algorithms:-


# Loading the files and creating dataframes
Data1 = pd.read_csv('Data1.csv')
Data2 = pd.read_csv('Data2.csv')

# Getting Shape and Size of each data
D1 = Data1.shape
D2 = Data2.shape

# Displaying Data1 Dataset
print('\033[1m1. Data1 dataset consist:-\033[0m\n Number of Rows    =',D1[0],'\n Number of Columns =',D1[1])
display(Data1.head())
print('_________________________________________________________________________________')

# Displaying Data2 Dataset
print('\033[1m\n2. Data2 dataset consist:-\033[0m\n Number of Rows    =',D2[0],'\n Number of Columns =',D2[1])
display(Data2.head())

1. Data1 dataset consist:-
 Number of Rows    = 5000 
 Number of Columns = 8

_________________________________________________________________________________

2. Data2 dataset consist:-
 Number of Rows    = 5000 
 Number of Columns = 7


# Merging two Datasets with common Attribute ID
bankdata = pd.merge(Data1, Data2, how='inner', on = 'ID')

# Getting Shape and Size of final dataset
BD = bankdata.shape

# Displaying Final Dataset
print('\033[1mFinal Dataset consist:-\033[0m\n Number of Rows    =',BD[0],'\n Number of Columns =',BD[1])
display(bankdata.head(10))

Final Dataset consist:-
 Number of Rows    = 5000 
 Number of Columns = 14


# Exploring dataset
print('\n\033[1mData Types of Each Attribute:-')
display(bankdata.dtypes.to_frame('Data Type'))

Data Types of Each Attribute:-


# Checking for Null Values in the Attributes
print('\n\033[1mNull Values in the Features:-')
display(bankdata.isnull().sum().to_frame('Null Values'))

Null Values in the Features:-


# Dropping Null Values
bankdata.dropna(inplace=True)

# Checking Shape of Dataset after dropping Null Values
shape = bankdata.shape
print('\033[1mAfter dropping Null values, Dataset consist:-\033[0m\n Number of Rows    =',shape[0],
      '\n Number of Columns =',shape[1])

# Checking for Null Values after dropping
print('\n\033[1mNull Values in the Attributes After Dropping:-')
display(bankdata.isnull().sum().to_frame('Null Values'))

After dropping Null values, Dataset consist:-
 Number of Rows    = 4980 
 Number of Columns = 14

Null Values in the Attributes After Dropping:-


# Changing Datatype of LoanOnCard Attribute
bankdata['LoanOnCard'] = bankdata['LoanOnCard'].astype('int')

# Exploring dataset after changing datatype
print('\n\033[1mData Types of Each Attribute:-')
display(bankdata.dtypes.to_frame('Data Type'))

Data Types of Each Attribute:-


# Describing the data interms of count, mean, standard deviation, and 5 point summary
print('\n\033[1mBrief Summary of Dataset:-')
display(bankdata.describe().T)

Brief Summary of Dataset:-


# Dropping ID and ZipCode Attributes
bankdata.drop(['ID','ZipCode'],axis=1,inplace=True)

# Getting Shape of data
bd = bankdata.shape

# Displaying Dataset after dropping columns
print('\033[1mFinal Dataset consist:-\033[0m\n Number of Rows    =',bd[0],'\n Number of Columns =',bd[1])
display(bankdata.head())

Final Dataset consist:-
 Number of Rows    = 4980 
 Number of Columns = 12


# Getting Negative Values in CustomerSince Attribute
CS = bankdata[bankdata['CustomerSince'] < 0]['CustomerSince']
print('\033[1mTotal Negative Values =',CS.count())
print('\033[1m\nTable Showing Negative values and its counts:-')
NCS = CS.value_counts().to_frame('Total Counts')
NCS.index.names = ['Negative CustomerSince']
display(NCS)

Total Negative Values = 51

Table Showing Negative values and its counts:-


# Converting Negative Values of CustomerSince to Positive
bankdata['CustomerSince'] = bankdata['CustomerSince'].abs()

# Getting Negative Values in CustomerSince Attribute after doing abs()
CS = bankdata[bankdata['CustomerSince'] < 0]['CustomerSince']
print('\033[1mTotal Negative Values          =',CS.count())

# Getting Minimum and Maximum Values of CustomerSince
print('\033[1mMinimum Value of CustomerSince =',bankdata['CustomerSince'].min())
print('\033[1mMaximum Value of CustomerSince =',bankdata['CustomerSince'].max())

Total Negative Values          = 0
Minimum Value of CustomerSince = 0
Maximum Value of CustomerSince = 43


# Checking skewness of the data attributes
print('\033[1mSkewness of all attributes:-')  
display(bankdata.skew().to_frame(name='Skewness'))

Skewness of all attributes:-


# Checking Covariance related with all independent attributes
print('\033[1mCovariance between all attributes:-')
display(bankdata.cov())

Covariance between all attributes:-


# Checking Correlation by plotting Heatmap for all independent attributes 
print('\033[1mHeatmap showing Correlation of Data attributes:-')
plt.figure(figsize=(14,14))
plt.title('Correlation of Data Attributes\n')
sns.heatmap(bankdata.corr(),annot=True,fmt= '.2f',cmap='magma');
plt.show()

Heatmap showing Correlation of Data attributes:-


# Plotting Distribution for attribute Age
print('\033[1mPlot Showing Distribution of Attribute Age:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of Age\n')
sns.distplot(bankdata['Age'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

print('\033[1mPlot Showing 5 point summary with outliers of Attribute Age:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for Age\n')
sns.boxplot(bankdata['Age'],color="#9400D3");
plt.show()

Plot Showing Distribution of Attribute Age:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute Age:-


# Plotting Distribution for attribute CustomerSince
print('\033[1mPlot Showing Distribution of Feature CustomerSince:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of CustomerSince\n')
sns.distplot(bankdata['CustomerSince'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

print('\033[1mPlot Showing 5 point summary with outliers of Attribute CustomerSince:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for CustomerSince\n')
sns.boxplot(bankdata['CustomerSince'],color="#9400D3");
plt.show()

Plot Showing Distribution of Feature CustomerSince:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute CustomerSince:-


# Plotting Distribution for attribute HighestSpend
print('\033[1mPlot Showing Distribution of Attribute HighestSpend:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of HighestSpend\n')
sns.distplot(bankdata['HighestSpend'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

print('\033[1mPlot Showing 5 point summary with outliers of Attribute HighestSpend:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for HighestSpend\n')
sns.boxplot(bankdata['HighestSpend'],color="#9400D3");
plt.show()

Plot Showing Distribution of Attribute HighestSpend:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute HighestSpend:-


# Plotting Frequency Distribution for attribute HiddenScore
colors = ['gold', 'yellowgreen', 'tomato','#ee99ff']
print('\033[1mPlot Showing Frequency Distribution of Feature HiddenScore:-')
plt.figure(figsize=(10,8))
plt.title('Frequencies of HiddenScore Attribute\n')
sns.countplot(bankdata['HiddenScore'],palette='bright');
plt.show()
print('___________________________________________________________________________________')
print('')

# Plotting Pie Chart to check contribution
print('\033[1mPie Chart Showing Contribution of Each Category of HiddenScore feature:-')
plt.title('Contribution of Each Category of HiddenScore Attribute\n\n\n\n\n\n')
explode = (0.09, 0.1, 0.1, 0.1)
bankdata['HiddenScore'].value_counts().plot.pie(radius=2.5,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-90,
                                                colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2, 1))
plt.show()

Plot Showing Frequency Distribution of Feature HiddenScore:-

___________________________________________________________________________________

Pie Chart Showing Contribution of Each Category of HiddenScore feature:-


# Plotting Distribution for attribute MonthlyAverageSpend
print('\033[1mPlot Showing Distribution of Feature MonthlyAverageSpend:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of MonthlyAverageSpend\n')
sns.distplot(bankdata['MonthlyAverageSpend'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

print('\033[1mPlot Showing 5 point summary with outliers of Attribute MonthlyAverageSpend:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for MonthlyAverageSpend\n')
sns.boxplot(bankdata['MonthlyAverageSpend'],color="#9400D3");
plt.show()

Plot Showing Distribution of Feature MonthlyAverageSpend:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute MonthlyAverageSpend:-


# Plotting Frequency Distribution for attribute Level
colors = ['gold', 'yellowgreen', 'tomato']
print('\033[1mPlot Showing Frequency Distribution of Attribute Level:-')
plt.figure(figsize=(10,8))
plt.title('Frequencies of Level Attribute\n')
sns.countplot(bankdata['Level'],palette='bright');
plt.show()
print('___________________________________________________________________________________')
print('')

# Plotting Pie Chart to check contribution
print('\033[1mPie Chart Showing Contribution of Each Category of Level feature:-')
plt.title('Contribution of Each Category of Level Attribute\n\n\n\n\n\n')
explode = (0.09, 0.1, 0.1)
bankdata['Level'].value_counts().plot.pie(radius=2.5,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-10,
                                          colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2, 1))
plt.show()

Plot Showing Frequency Distribution of Attribute Level:-

___________________________________________________________________________________

Pie Chart Showing Contribution of Each Category of Level feature:-


# Plotting Distribution for attribute Mortgage
print('\033[1mPlot Showing Distribution of Feature Mortgage:-')
plt.figure(figsize=(12,6))
plt.title('Distribution of Mortgage\n')
sns.distplot(bankdata['Mortgage'],color='#9400D3');
print('')
plt.show()
print('__________________________________________________________________________________________________')
print('')

print('\033[1mPlot Showing 5 point summary with outliers of Attribute Mortgage:-')
plt.figure(figsize=(12,6))
plt.title('Box Plot for Mortgage\n')
sns.boxplot(bankdata['Mortgage'],color="#9400D3");
plt.show()

Plot Showing Distribution of Feature Mortgage:-

__________________________________________________________________________________________________

Plot Showing 5 point summary with outliers of Attribute Mortgage:-


# Plotting Pie Chart to check contribution Security Attribute
colors = ['tomato','gold']
labels = ['No Customer’s Security Asset','There is Customer’s Security Asset']
print('\033[1mPie Chart Showing Contribution of Each Category of Security feature:-')
plt.title('Contribution of Each Category of Security Attribute\n\n\n\n\n\n')
explode = (0.09, 0.1)
bankdata['Security'].value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',labels=labels,
                                             colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Pie Chart Showing Contribution of Each Category of Security feature:-


# Plotting Pie Chart to check contribution FixedDepositAccount Attribute
labels = ['Customers do not have FixedDepositAccount','Customers have FixedDepositAccount']
print('\033[1mPie Chart Showing Contribution of Each Category of FixedDepositAccount feature:-')
plt.title('Contribution of Each Category of FixedDepositAccount Attribute\n\n\n\n\n\n')
explode = (0.2, 0.2)
bankdata['FixedDepositAccount'].value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-30,
                                                labels=labels,colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.7, 1))
plt.show()

Pie Chart Showing Contribution of Each Category of FixedDepositAccount feature:-


# Plotting Pie Chart to check contribution of InternetBanking Attribute
colors = ['gold','tomato']
labels = ['Customers use Internet Banking','Customers do not use Internet Banking']
print('\033[1mPie Chart Showing Contribution of Each Category of InternetBanking feature:-')
plt.title('Contribution of Each Category of InternetBanking Attribute\n\n\n\n\n\n')
explode = (0.05, 0.05)
bankdata['InternetBanking'].value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',labels=labels,
                                                    colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Pie Chart Showing Contribution of Each Category of InternetBanking feature:-


# Plotting Pie Chart to check contribution of CreditCard Attribute
colors = ['tomato','gold']
labels = ['Customer do not uses Bank’s Credit Card','Customer uses Bank’s Credit Card']
print('\033[1mPie Chart Showing Contribution of Each Category of CreditCard feature:-')
plt.title('Contribution of Each Category of CreditCard Attribute\n\n\n\n\n\n')
explode = (0.05, 0.05)
bankdata['CreditCard'].value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',labels=labels,
                                               colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Pie Chart Showing Contribution of Each Category of CreditCard feature:-


# Plotting Pie Chart to check contribution of LoanOnCard Attribute
labels = ['Customers do not have Loan on Credit Card','Customers have Loan on Credit Card']
print('\033[1mPie Chart Showing Contribution of Each Category of LoanOnCard feature:-')
plt.title('Contribution of Each Category of LoanOnCard Attribute\n\n\n\n\n\n')
explode = (0.1, 0.1)
bankdata['LoanOnCard'].value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-30,
                                               labels=labels, colors=colors);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2.6, 1))
plt.show()

Pie Chart Showing Contribution of Each Category of LoanOnCard feature:-


# Getting Number of Outliers or Extreme values in attributes
clm = ['Age','CustomerSince','HighestSpend','MonthlyAverageSpend','Mortgage']
AT = []
OL = []
for i in clm:
    AT.append(i)
    q1 = bankdata[i].quantile(0.25)
    q3 = bankdata[i].quantile(0.75)
    IQR = q3 - q1
    OL_ctr=0
    for k in bankdata[i]:
        if (k < (q1 - 1.5 * IQR) or k > (q3 + 1.5 * IQR)):
            OL_ctr+=1
    OL.append(OL_ctr)
print('\033[1mTable Showing Number of Outiers in Attributes:-')
Outlier = pd.DataFrame(AT,columns=['Attribute'])
Outlier['Outliers'] = OL
display(Outlier)

Table Showing Number of Outiers in Attributes:-


# Bivariate Analysis for Age VS All Categorical Attributes
print('\033[1m\nPlots Showing Bivariate Analysis of Age VS All Categorical Attributes:-')

# Setting up Sub-Plots
fig, axes = plt.subplots(4, 2, figsize=(15, 17))
fig.suptitle('Age VS All Categorical Attributes')
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.94, wspace=0.3, hspace=0.4)

# Plotting Sub-Plots
sns.violinplot(ax=axes[0, 0], x='HiddenScore', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[0, 1], x='Level', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 0], x='Security', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 1], x='FixedDepositAccount', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 0], x='InternetBanking', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 1], x='CreditCard', y='Age', data=bankdata, palette='bright');
sns.violinplot(ax=axes[3, 0], x='LoanOnCard', y='Age', data=bankdata, palette='bright');
plt.show()


Plots Showing Bivariate Analysis of Age VS All Categorical Attributes:-


# Bivariate Analysis for CustomerSince VS All Categorical Attributes
print('\033[1m\nPlots Showing Bivariate Analysis of CustomerSince VS All Categorical Attributes:-')

# Setting up Sub-Plots
fig, axes = plt.subplots(4, 2, figsize=(15, 17))
fig.suptitle('CustomerSince VS All Categorical Attributes')
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.94, wspace=0.3, hspace=0.4)

# Plotting Sub-Plots
sns.violinplot(ax=axes[0, 0], x='HiddenScore', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[0, 1], x='Level', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 0], x='Security', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 1], x='FixedDepositAccount', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 0], x='InternetBanking', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 1], x='CreditCard', y='CustomerSince', data=bankdata, palette='bright');
sns.violinplot(ax=axes[3, 0], x='LoanOnCard', y='CustomerSince', data=bankdata, palette='bright');
plt.show()


Plots Showing Bivariate Analysis of CustomerSince VS All Categorical Attributes:-


# Bivariate Analysis for HighestSpend VS All Categorical Attributes
print('\033[1m\nPlots Showing Bivariate Analysis of HighestSpend VS All Categorical Attributes:-')

# Setting up Sub-Plots
fig, axes = plt.subplots(4, 2, figsize=(15, 17))
fig.suptitle('HighestSpend VS All Categorical Attributes')
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.94, wspace=0.3, hspace=0.4)

# Plotting Sub-Plots
sns.violinplot(ax=axes[0, 0], x='HiddenScore', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[0, 1], x='Level', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 0], x='Security', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 1], x='FixedDepositAccount', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 0], x='InternetBanking', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 1], x='CreditCard', y='HighestSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[3, 0], x='LoanOnCard', y='HighestSpend', data=bankdata, palette='bright');
plt.show()


Plots Showing Bivariate Analysis of HighestSpend VS All Categorical Attributes:-


# Bivariate Analysis for MonthlyAverageSpend VS All Categorical Attributes
print('\033[1m\nPlots Showing Bivariate Analysis of MonthlyAverageSpend VS All Categorical Attributes:-')

# Setting up Sub-Plots
fig, axes = plt.subplots(4, 2, figsize=(15, 17))
fig.suptitle('MonthlyAverageSpend VS All Categorical Attributes')
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.94, wspace=0.3, hspace=0.4)

# Plotting Sub-Plots
sns.violinplot(ax=axes[0, 0], x='HiddenScore', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[0, 1], x='Level', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 0], x='Security', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 1], x='FixedDepositAccount', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 0], x='InternetBanking', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 1], x='CreditCard', y='MonthlyAverageSpend', data=bankdata, palette='bright');
sns.violinplot(ax=axes[3, 0], x='LoanOnCard', y='MonthlyAverageSpend', data=bankdata, palette='bright');
plt.show()


Plots Showing Bivariate Analysis of MonthlyAverageSpend VS All Categorical Attributes:-


# Bivariate Analysis for Mortgage VS All Categorical Attributes
print('\033[1m\nPlots Showing Bivariate Analysis of Mortgage VS All Categorical Attributes:-')

# Setting up Sub-Plots
fig, axes = plt.subplots(4, 2, figsize=(15, 17))
fig.suptitle('Mortgage VS All Categorical Attributes')
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.94, wspace=0.3, hspace=0.4)

# Plotting Sub-Plots
sns.violinplot(ax=axes[0, 0], x='HiddenScore', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[0, 1], x='Level', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 0], x='Security', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[1, 1], x='FixedDepositAccount', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 0], x='InternetBanking', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[2, 1], x='CreditCard', y='Mortgage', data=bankdata, palette='bright');
sns.violinplot(ax=axes[3, 0], x='LoanOnCard', y='Mortgage', data=bankdata, palette='bright');
plt.show()


Plots Showing Bivariate Analysis of Mortgage VS All Categorical Attributes:-


# Multivariate Analysis of Attributes
print('\033[1mPlot Showing Multivariate Analysis to check Relation between Discrete Attributes:-')

# Getting Discrete Attributes and creating dataframe of it
dis_att = bankdata[['Age','CustomerSince','HighestSpend','MonthlyAverageSpend','Mortgage']]

# Plotting pairplot for Discrete Attributes
sns.pairplot(dis_att,plot_kws={'color':'#9400D3'},diag_kws={'color':'Gold'}).fig.suptitle(
    'Relation Between Discrete Attributes',y=1.04);
plt.show()

Plot Showing Multivariate Analysis to check Relation between Discrete Attributes:-


# Multivariate Analysis to check density of Target Attribute in all other Attributes
print('\033[1mPlot Showing Multivariate Analysis to check Density of Target Attribute in all other Attributes:-')

# Adding Target column in dis_att dataframe
dis_att['LoanOnCard'] = bankdata['LoanOnCard']

# Plotting Pair plot for Discrete features with Target attribute
sns.pairplot(dis_att, hue='LoanOnCard', palette='bright').fig.suptitle('Density of Target Attribute',y=1.04);
plt.show()

Plot Showing Multivariate Analysis to check Density of Target Attribute in all other Attributes:-


# Plotting Heatmap for checking Correlation
print('\033[1mHeatmap showing Correlation of Data attributes:-')
plt.figure(figsize=(14,14))
plt.title('Correlation of Data Attributes\n')
sns.heatmap(bankdata.corr(),annot=True,fmt= '.2f',cmap='Spectral');
plt.show()

Heatmap showing Correlation of Data attributes:-


# Getting Outliers and Imputing Outliers by Mean

# Creating list of Discrete Feature column with additional empty lists that are required
clm = ['Age','CustomerSince','HighestSpend','MonthlyAverageSpend','Mortgage']
AT = []
OL1 = []
OL2 = []
M1 = []
M2 = []
for i in clm:
    AT.append(i)
        
    # Getting Interquartile Range
    q1 = bankdata[i].quantile(0.25)
    q3 = bankdata[i].quantile(0.75)
    IQR = q3 - q1
        
    # Getting Mean of each Attribute having Outliers (i.e including outliers)
    M1.append(round(bankdata[i].mean(),2)) 
        
    # Getting Outlier and Normal Values Seperated
    OL = []
    NOL = []
    for k in bankdata[i]:
        if (k < (q1 - 1.5 * IQR) or k > (q3 + 1.5 * IQR)):
            OL.append(k)
        else:
            NOL.append(k)
    OL1.append(len(OL))
        
    # Replacing Outliers by Mean of Normal Values
    bankdata[i].replace(OL,np.mean(NOL),inplace=True) # Here we are imputing outliers by Mean of attribute without outlier
    M2.append(round(np.mean(NOL),2))
        
    # Getting Outliers After Imputation
    OL_cnt = 0
    for k in bankdata[i]:
        if (k < (q1 - 1.5 * IQR) or k > (q3 + 1.5 * IQR)):
            OL_cnt += 1
    OL2.append(OL_cnt)
        
# Creting dataframe for better representation of Outlier Analysis      
Outlier_Analysis = pd.DataFrame({'Attribute':AT,
                                 'Mean Including Outliers':M1,
                                 'Outliers Before Imputation':OL1,
                                 'Mean Excluding Outliers':M2,
                                 'Outliers After Imputation':OL2})
print('\033[1mTotal Outliers Observed in Discrete Attributes =',sum(OL1))
print('\n\033[1mTable Showing Outlier Analysis:-')
display(Outlier_Analysis)

Total Outliers Observed in Discrete Attributes = 711

Table Showing Outlier Analysis:-


# Creating list of Discrete Feature column 
clm = ['Age','CustomerSince','HighestSpend','MonthlyAverageSpend','Mortgage']

# Applying Z-Scores to Discrete features
bankdata[clm] = bankdata[clm].apply(zscore)

# Checking the Mean and Standard Deviation
print('\033[1mTable Showing Mean and Standard Deviation of Scaled Discrete Attributes:-')
display(bankdata[['Age','CustomerSince','HighestSpend','MonthlyAverageSpend','Mortgage']].describe()[1:3].T)

Table Showing Mean and Standard Deviation of Scaled Discrete Attributes:-


# Seperating Independent and Dependent Attributes

# Getting Predictors by dropping Class Attribute
X = bankdata.drop(columns='LoanOnCard')

# Getting Target Attribute
y = bankdata['LoanOnCard'] 

print('\033[1mTable Showing Segregated Predictors:-')
display(X.head())

Table Showing Segregated Predictors:-


# Checking Value Counts of Target Attribute
print('\033[1mTable Showing Total Observations in each section:-')
TAC = y.value_counts().to_frame('Total Observations')
display(TAC)

# Getting Percentages of each category in Target Attribute

print('\033[1m\n\nPie Chart Showing Percentage of Each Category of Target Attribute:-')
plt.title('Percentage of Each Category of Target Attribute\n\n\n\n\n\n')
explode = (0.05, 0.1)
y.value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',startangle=-90,colors=['yellowgreen','gold']);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2, 1))
plt.show()

Table Showing Total Observations in each section:-



Pie Chart Showing Percentage of Each Category of Target Attribute:-


# Getting total observations of target attribute before transformation
yct = y.count()

# Transforming the dataset
OS = SMOTE(random_state=1)
X, y = OS.fit_resample(X, y)

# Checking Value Counts of Target Attribute after transforming
print('\033[1mTable Showing Total Observations in each section of target data for SMOTE:-') 
TAC2 = y.value_counts().to_frame('Total Observations')

# For better representation
TVC = pd.DataFrame({'Before Tranformation':TAC['Total Observations'],'After Tranformation':TAC2['Total Observations']})
total  = pd.Series({'Before Tranformation':yct,'After Tranformation':y.count()},name='Total')
TVC = TVC.append(total)
columns=[('__________Total Observations__________', 'Before Tranformation'), ('__________Total Observations__________', 
                                                                              'After Tranformation')]
TVC.columns = pd.MultiIndex.from_tuples(columns)

display(TVC)

# Getting Percentages of each category in Target Attribute

print('\033[1m\n\nPie Chart Showing Percentage of Each Category of Target Attribute:-')
plt.title('Percentage of Each Category of Target Attribute\n\n\n\n\n\n')
explode = (0.05, 0.1)
y.value_counts().plot.pie(radius=2,explode=explode,shadow=True,autopct='%1.1f%%',colors=['yellowgreen','gold']);
plt.legend(loc='right',prop={'size': 12}, bbox_to_anchor=(2, 1))
plt.show()

Table Showing Total Observations in each section of target data for SMOTE:-



Pie Chart Showing Percentage of Each Category of Target Attribute:-


# Splitting into Train and Test Sets in 

# Here test_size is not given because by default its value is 0.25.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y)

# For better observation of Splitted Data
TTS = pd.DataFrame({'Train':y_train.value_counts(),'Test':y_test.value_counts(),'Total Observations':y.value_counts()})
total  = pd.Series({'Train':y_train.count(),'Test':y_test.count(),'Total Observations':y.shape[0]},name='Total')
TTS = TTS.append(total)
print('\033[1mTable Showing Train-Test Split of Data:-')
display(TTS)

Table Showing Train-Test Split of Data:-


# Building Logistic Regression Model
LR = LogisticRegression(solver="liblinear")

# Fitting Logistic Regression Model on Train
LR.fit(X_train, y_train)

# Predicting on the Train and Test set
LR_Prediction_Train = LR.predict(X_train)
LR_Prediction_Test = LR.predict(X_test)


# Building Gaussian Naive Bayes Model
NB = GaussianNB()

# Fitting Gaussian Naive Bayes Model on Train
NB.fit(X_train, y_train)

# Predicting on the Train and Test set
NB_Prediction_Train = NB.predict(X_train)
NB_Prediction_Test = NB.predict(X_test)


# Finding Logistic Regression Model Accuracies for Train and Test
Train_LRAC = round(metrics.accuracy_score(y_train,LR_Prediction_Train)*100,2)
Test_LRAC = round(metrics.accuracy_score(y_test,LR_Prediction_Test)*100,2)

# Displaying Logistic Regression Model Accuracies for Train and Test Data
print('\033[1mTable Showing Logistic Regression Model Accuracies for Train and Test Data:-')
display(pd.DataFrame({'Data':['Training','Testing'],'LR Accuracy (%)':[Train_LRAC,Test_LRAC]}).set_index('Data'))

# Finding Gaussian Naive Bayes Model Accuracies for Train and Test
Train_NBAC = round(metrics.accuracy_score(y_train,NB_Prediction_Train)*100,2)
Test_NBAC = round(metrics.accuracy_score(y_test,NB_Prediction_Test)*100,2)

# Displaying Gaussian Naive Bayes Model Accuracies for Train and Test Data
print('\033[1m\n\nTable Showing Gaussian Naive Bayes Model Accuracies for Train and Test Data:-')
display(pd.DataFrame({'Data':['Training','Testing'],'NB Accuracy (%)':[Train_NBAC,Test_NBAC]}).set_index('Data'))

Table Showing Logistic Regression Model Accuracies for Train and Test Data:-



Table Showing Gaussian Naive Bayes Model Accuracies for Train and Test Data:-


# Getting Classification Report for Logistic Regression Model
LRCR = metrics.classification_report(y_test,LR_Prediction_Test,output_dict=True)

# Displaying Logistic Regression Model Classification Report
print('\033[1mTable Showing Logistic Regression Model Classification Report:-')
display(pd.DataFrame(LRCR))

# Getting Classification Report for Naive Bayes Model 
NBCR = metrics.classification_report(y_test,NB_Prediction_Test,output_dict=True)

# Displaying Naive Bayes Model Classification Report
print('\033[1m\n\nTable Showing Naive Bayes Model Classification Report:-')
display(pd.DataFrame(NBCR))

Table Showing Logistic Regression Model Classification Report:-



Table Showing Naive Bayes Model Classification Report:-


# Building Confusion Matrix for Logistic Regression Model
CM1 = metrics.confusion_matrix(y_test, LR_Prediction_Test)
Con_Mat1 = pd.DataFrame(CM1)

# Displaying Confusion Matrix for Logistic Regression Model
print('\033[1m Heatmap Showing Performance of Logistic Regression Model:-')
plt.figure(figsize = (7,5))
sns.heatmap(Con_Mat1, annot=True, fmt=".1f")
plt.title('Confusion Matrix of Logistic Regression Model\n')
plt.xlabel('\nPredicted Labels\n')
plt.ylabel('Actual Labels\n')
plt.show()

# Building Confusion Matrix for Naive Bayes Model
CM2 = metrics.confusion_matrix(y_test, NB_Prediction_Test)
Con_Mat2 = pd.DataFrame(CM2)

# Displaying Confusion Matrix for Naive Bayes Model
print('\033[1m\n Heatmap Showing Performance of Naive Bayes Model:-')
plt.figure(figsize = (7,5))
sns.heatmap(Con_Mat2, annot=True, fmt=".1f")
plt.title('Confusion Matrix of Naive Bayes Model\n')
plt.xlabel('\nPredicted Labels\n')
plt.ylabel('Actual Labels\n')
plt.show()

 Heatmap Showing Performance of Logistic Regression Model:-


 Heatmap Showing Performance of Naive Bayes Model:-


# Creating List of Models
Models = [LogisticRegression(), KNeighborsClassifier(metric='euclidean'), GaussianNB(), SVC(gamma='auto'),
          SVC(kernel='linear',gamma='auto'), RandomForestClassifier(), DecisionTreeClassifier()]

# Getting Accuracies and Error Rates from Models
Model_Accuracy = []
Model_Error = []
for i in Models:
    Model_Accuracy.append(round(cross_val_score(i,X,y,scoring='accuracy',cv=5).mean(),3)*100)
    Model_Error.append(round(1-cross_val_score(i,X,y,scoring='accuracy',cv=5).mean(),3)*100)
    
# Displaying Accuracies and Error Rates from Models
print('\033[1mTable Showing Accuracies and Error Rates from Varius Algorithms:-')
model = ['Logistic Regression','K-Neighbors Classifier','Gaussian Naive Bayes','RBF Support Vector Classifier',
        'Linear Support Vector Classifier','Random Forest Classifier','Decision Tree Classifier']
display(pd.DataFrame({'Trained Model':model,'Accuracy (%)':Model_Accuracy,'Error Rate (%)':Model_Error}))

Table Showing Accuracies and Error Rates from Varius Algorithms:-

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree	Class
0	38.505273	16.964297	35.112814	21.540976	127.632875	7.986683	Normal
1	54.920858	18.968430	51.601455	35.952428	125.846646	2.001642	Normal
2	44.362490	8.945435	46.902096	35.417055	129.220682	4.994195	Normal
3	48.318931	17.452121	48.000000	30.866809	128.980308	-0.910941	Normal
4	45.701789	10.659859	42.577846	35.041929	130.178314	-3.388910	Normal

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree	Class
0	63.027818	22.552586	39.609117	40.475232	98.672917	-0.254400	Type_H
1	39.056951	10.060991	25.015378	28.995960	114.405425	4.564259	Type_H
2	68.832021	22.218482	50.092194	46.613539	105.985135	-3.530317	Type_H
3	69.297008	24.652878	44.311238	44.644130	101.868495	11.211523	Type_H
4	49.712859	9.652075	28.317406	40.060784	108.168725	7.918501	Type_H

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree	Class
0	74.377678	32.053104	78.772013	42.324573	143.560690	56.125906	Type_S
1	89.680567	32.704435	83.130732	56.976132	129.955476	92.027277	Type_S
2	44.529051	9.433234	52.000000	35.095817	134.711772	29.106575	Type_S
3	77.690577	21.380645	64.429442	56.309932	114.818751	26.931841	Type_S
4	76.147212	21.936186	82.961502	54.211027	123.932010	10.431972	Type_S

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree	Class
0	38.505273	16.964297	35.112814	21.540976	127.632875	7.986683	Normal
1	54.920858	18.968430	51.601455	35.952428	125.846646	2.001642	Normal
2	44.362490	8.945435	46.902096	35.417055	129.220682	4.994195	Normal
3	48.318931	17.452121	48.000000	30.866809	128.980308	-0.910941	Normal
4	45.701789	10.659859	42.577846	35.041929	130.178314	-3.388910	Normal
5	30.741938	13.354966	35.903526	17.386972	142.410107	-2.005373	Normal
6	50.913101	6.677000	30.896522	44.236102	118.151531	-1.057986	Normal
7	38.126589	6.557617	50.445075	31.568971	132.114805	6.338199	Normal
8	51.624672	15.969344	35.000000	35.655328	129.385308	1.009228	Nrmal
9	64.311867	26.328369	50.958964	37.983498	106.177751	3.118221	Nrmal

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree
count	310.000000	310.000000	310.000000	310.000000	310.000000	310.000000
mean	60.496653	17.542822	51.930930	42.953831	117.920655	26.296694
std	17.236520	10.008330	18.554064	13.423102	13.317377	37.559027
min	26.147921	-6.554948	14.000000	13.366931	70.082575	-11.058179
25%	46.430294	10.667069	37.000000	33.347122	110.709196	1.603727
50%	58.691038	16.357689	49.562398	42.404912	118.268178	11.767934
75%	72.877696	22.120395	63.000000	52.695888	125.467674	41.287352
max	129.834041	49.431864	125.742385	121.429566	163.071041	418.543082

	Data Type
P_incidence	float64
P_tilt	float64
L_angle	float64
S_slope	float64
P_radius	float64
S_Degree	float64
Class	object

	Class	Normal	Type_H	Type_S
P_incidence	count	100.000000	60.000000	150.000000
	mean	51.685244	47.638407	71.514224
	std	12.368161	10.697131	15.109337
	min	30.741938	26.147921	37.903910
	25%	42.817849	41.018099	60.658136
	50%	50.123115	46.416169	72.149306
	75%	61.470097	53.922329	81.075893
	max	89.834676	74.433593	129.834041
P_tilt	count	100.000000	60.000000	150.000000
	mean	12.821414	17.398795	20.748038
	std	6.778503	7.016708	11.506169
	min	-5.845994	3.144669	-6.554948
	25%	8.799951	12.874329	13.461149
	50%	13.482435	16.949285	19.309548
	75%	16.785953	22.002980	29.229680
	max	29.894119	41.557331	49.431864
L_angle	count	100.000000	60.000000	150.000000
	mean	43.542605	35.463524	64.110108
	std	12.361388	9.767795	16.397068
	min	19.071075	14.000000	24.710274
	25%	35.000000	29.038069	52.000000
	50%	42.638923	35.164873	62.555200
	75%	51.602346	42.011563	76.990924
	max	90.563461	62.275275	125.742385
S_slope	count	100.000000	60.000000	150.000000
	mean	38.863830	30.239612	50.766186
	std	9.624004	7.555388	12.318813
	min	17.386972	13.366931	19.290046
	25%	32.340487	25.215275	43.166815
	50%	37.059694	30.000659	50.751695
	75%	44.608788	34.962103	56.675435
	max	67.195460	46.613539	121.429566
P_radius	count	100.000000	60.000000	150.000000
	mean	123.890834	116.474968	114.518810
	std	9.014246	9.355720	15.579995
	min	100.501192	84.241415	70.082575
	25%	118.182659	112.280320	104.706761
	50%	123.874328	116.696146	114.842400
	75%	129.040401	122.042999	123.337307
	max	147.894637	137.540613	163.071041
S_Degree	count	100.000000	60.000000	150.000000
	mean	2.186572	2.480251	51.896687
	std	6.307483	5.531177	40.108030
	min	-11.058179	-10.675871	1.010041
	25%	-1.511360	-0.910819	30.393271
	50%	1.152710	2.542441	42.372509
	75%	4.968807	6.059666	61.894425
	max	31.172767	15.779697	418.543082

	Skewness
P_incidence	0.520440
P_tilt	0.676553
L_angle	0.599451
S_slope	0.792577
P_radius	-0.176835
S_Degree	4.317954

	Interquartile Range
P_incidence	26.447401
P_tilt	11.453326
L_angle	26.000000
S_slope	19.348766
P_radius	14.758478
S_Degree	39.683625

	P_incidence	P_tilt	L_angle	S_slope	P_radius	S_Degree
P_incidence	297.097633	108.542318	229.392280	188.555315	-56.804919	413.513704
P_tilt	108.542318	100.166675	80.362172	8.375643	4.354120	149.557681
L_angle	229.392280	80.362172	344.253290	149.030108	-19.852220	371.897908
S_slope	188.555315	8.375643	149.030108	180.179672	-61.159039	263.956023
P_radius	-56.804919	4.354120	-19.852220	-61.159039	177.352531	-13.037396
S_Degree	413.513704	149.557681	371.897908	263.956023	-13.037396	1410.680476

	Attribute	Mean Including Outliers	Outliers Before Imputation	Mean Excluding Outliers
0	P_incidence	60.50	3	59.90
1	P_tilt	17.54	13	16.57
2	L_angle	51.93	1	51.69
3	S_slope	42.95	1	42.70
4	P_radius	117.92	11	118.06
5	S_Degree	26.30	10	22.20

	mean	std
P_incidence	-1.923193e-16	1.001617
P_tilt	2.339079e-17	1.001617
L_angle	-1.196176e-16	1.001617
S_slope	-1.636684e-16	1.001617
P_radius	-1.147469e-15	1.001617
S_Degree	-2.918812e-16	1.001617

	__________Total Observations__________
	Before Tranformation	After Tranformation
0	100	150
1	60	150
2	150	150
Total	310	450

	0	1	2	accuracy	macro avg	weighted avg
precision	0.852941	0.739130	1.000000	0.849558	0.864024	0.864122
recall	0.783784	0.894737	0.868421	0.849558	0.848981	0.849558
f1-score	0.816901	0.809524	0.929577	0.849558	0.852001	0.852312
support	37.000000	38.000000	38.000000	0.849558	113.000000	113.000000

	Accuracy (%)		_______Precision_______
	Train	Test	Normal	Type_H	Type_S
K-Value
1	100.00	95.58	0.93	0.97	0.97
3	90.50	92.92	0.92	0.88	1.00
5	87.24	84.96	0.85	0.74	1.00
7	87.83	83.19	0.87	0.70	1.00
9	85.46	82.30	0.83	0.69	1.00
11	84.87	82.30	0.89	0.68	1.00
13	83.98	82.30	0.86	0.69	1.00
15	85.16	82.30	0.87	0.69	1.00
17	84.27	85.84	0.91	0.73	1.00
19	81.90	83.19	0.89	0.69	1.00

	Trained Model	Accuracy (%)	Error Rate (%)
0	Logistic Regression	85.6	14.4
1	K-Neighbors Classifier	87.6	12.4
2	Gaussian Naive Bayes	81.6	18.4
3	RBF Support Vector Classifier	86.2	13.8
4	Linear Support Vector Classifier	86.4	13.6
5	Random Forest Classifier	90.4	9.6
6	Decision Tree Classifier	85.3	14.7

	ID	Age	CustomerSince	HighestSpend	ZipCode	HiddenScore	MonthlyAverageSpend	Level
0	1	25	1	49	91107	4	1.6	1
1	2	45	19	34	90089	3	1.5	1
2	3	39	15	11	94720	1	1.0	1
3	4	35	9	100	94112	1	2.7	2
4	5	35	8	45	91330	4	1.0	2

	ID	Age	CustomerSince	HighestSpend	ZipCode	HiddenScore	MonthlyAverageSpend	Level	Mortgage	Security	InternetBanking	CreditCard	LoanOnCard
0	1	25	1	49	91107	4	1.6	1	0	1	0	0	NaN
1	2	45	19	34	90089	3	1.5	1	0	1	0	0	NaN
2	3	39	15	11	94720	1	1.0	1	0	0	0	0	NaN
3	4	35	9	100	94112	1	2.7	2	0	0	0	0	NaN
4	5	35	8	45	91330	4	1.0	2	0	0	0	1	NaN
5	6	37	13	29	92121	4	0.4	2	155	0	1	0	NaN
6	7	53	27	72	91711	2	1.5	2	0	0	1	0	NaN
7	8	50	24	22	93943	1	0.3	3	0	0	0	1	NaN
8	9	35	10	81	90089	3	0.6	2	104	0	1	0	NaN
9	10	34	9	180	93023	1	8.9	3	0	0	0	0	1.0

	Data Type
ID	int64
Age	int64
CustomerSince	int64
HighestSpend	int64
ZipCode	int64
HiddenScore	int64
MonthlyAverageSpend	float64
Level	int64
Mortgage	int64
Security	int64
FixedDepositAccount	int64
InternetBanking	int64
CreditCard	int64
LoanOnCard	float64

	count	mean	std	min	25%	50%	75%	max
ID	4980.0	2510.345382	1438.011129	10.0	1265.75	2510.5	3755.250	5000.0
Age	4980.0	45.352610	11.464212	23.0	35.00	45.0	55.000	67.0
CustomerSince	4980.0	20.117671	11.468716	-3.0	10.00	20.0	30.000	43.0
HighestSpend	4980.0	73.852410	46.070090	8.0	39.00	64.0	98.000	224.0
ZipCode	4980.0	93152.420482	2123.660073	9307.0	91911.00	93407.0	94608.000	96651.0
HiddenScore	4980.0	2.395582	1.147200	1.0	1.00	2.0	3.000	4.0
MonthlyAverageSpend	4980.0	1.939536	1.750006	0.0	0.70	1.5	2.525	10.0
Level	4980.0	1.880924	0.840144	1.0	1.00	2.0	3.000	3.0
Mortgage	4980.0	56.589759	101.836758	0.0	0.00	0.0	101.000	635.0
Security	4980.0	0.104418	0.305832	0.0	0.00	0.0	0.000	1.0
FixedDepositAccount	4980.0	0.060643	0.238697	0.0	0.00	0.0	0.000	1.0
InternetBanking	4980.0	0.597189	0.490513	0.0	0.00	1.0	1.000	1.0
CreditCard	4980.0	0.294378	0.455808	0.0	0.00	0.0	1.000	1.0
LoanOnCard	4980.0	0.096386	0.295149	0.0	0.00	0.0	0.000	1.0

	Age	CustomerSince	HighestSpend	HiddenScore	MonthlyAverageSpend	Level	Security	InternetBanking	LoanOnCard
9	34	9	180	1	8.9	3	0	0	1
10	65	39	105	4	2.4	3	0	0	0
11	29	5	45	3	0.1	2	0	1	0
12	48	23	114	2	3.8	3	1	0	0
13	59	32	40	4	2.5	2	0	1	0

	Skewness
Age	-0.030794
CustomerSince	-0.011746
HighestSpend	0.839524
HiddenScore	0.156413
MonthlyAverageSpend	1.596001
Level	0.227298
Mortgage	2.102030
Security	2.587962
FixedDepositAccount	3.682770
InternetBanking	-0.396433
CreditCard	0.902595
LoanOnCard	2.736088

	Age	CustomerSince	HighestSpend	HiddenScore	MonthlyAverageSpend	Level	Mortgage	Security	FixedDepositAccount	InternetBanking	CreditCard	LoanOnCard
Age	131.428161	130.092827	-29.022661	-0.595630	-1.041168	0.411749	-15.494802	0.001133	0.021191	0.063132	0.038376	-0.027566
CustomerSince	130.092827	130.334568	-24.416885	-0.665065	-0.990641	0.146496	-13.815000	-0.000732	0.025806	0.064604	0.044773	-0.029272
HighestSpend	-29.022661	-24.416885	2122.453208	-8.369401	52.091215	-7.311814	972.275050	-0.032186	1.864345	0.320935	-0.058372	6.834473
HiddenScore	-0.595630	-0.665065	-8.369401	1.316068	-0.219190	0.063382	-2.499664	0.006688	0.003923	0.006133	0.005639	0.020912
MonthlyAverageSpend	-1.041168	-0.990641	52.091215	-0.219190	3.062522	-0.201455	19.652732	0.008084	0.056981	-0.002983	-0.005246	0.189514
Level	0.411749	0.146496	-7.311814	0.063382	-0.201455	0.705842	-2.811664	-0.002426	0.002804	-0.005999	-0.004506	0.033974
Mortgage	-15.494802	-13.815000	972.275050	-2.499664	19.652732	-2.811664	10370.725221	-0.155789	2.167482	-0.351867	-0.352799	4.266502
Security	0.001133	-0.000732	-0.032186	0.006688	0.008084	-0.002426	-0.155789	0.093533	0.023191	0.002101	-0.002024	0.001984
FixedDepositAccount	0.021191	0.025806	1.864345	0.003923	0.056981	0.002804	2.167482	0.023191	0.056976	0.020616	0.030347	0.022272
InternetBanking	0.063132	0.064604	0.320935	0.006133	-0.002983	-0.005999	-0.351867	0.002101	0.020616	0.240603	0.001109	0.000874
CreditCard	0.038376	0.044773	-0.058372	0.005639	-0.005246	-0.004506	-0.352799	-0.002024	0.030347	0.001109	0.207761	0.000341
LoanOnCard	-0.027566	-0.029272	6.834473	0.020912	0.189514	0.033974	4.266502	0.001984	0.022272	0.000874	0.000341	0.087113

	Attribute	Mean Including Outliers	Outliers Before Imputation	Mean Excluding Outliers
0	Age	45.35	0	45.35
1	CustomerSince	20.15	0	20.15
2	HighestSpend	73.85	96	71.48
3	MonthlyAverageSpend	1.94	324	1.60
4	Mortgage	56.59	291	38.03

	mean	std
Age	-1.581065e-16	1.0001
CustomerSince	1.465138e-16	1.0001
HighestSpend	2.225239e-16	1.0001
MonthlyAverageSpend	2.918237e-17	1.0001
Mortgage	-2.358667e-16	1.0001

	Age	CustomerSince	HighestSpend	HiddenScore	MonthlyAverageSpend	Level	Mortgage	Security	InternetBanking
9	-0.990365	-0.976533	2.533781	1	0.000000	3	-0.575286	0	0
10	1.713974	1.651526	0.782680	4	0.696823	3	-0.575286	0	0
11	-1.426548	-1.326941	-0.618201	3	-1.301976	2	-0.575286	0	1
12	0.230950	0.249894	0.992812	2	1.913483	3	-0.575286	1	0
13	1.190554	1.038312	-0.734941	4	0.783727	2	-0.575286	0	1

	__________Total Observations__________
	Before Tranformation	After Tranformation
0	4500	4500
1	480	4500
Total	4980	9000

	0	1	accuracy	macro avg	weighted avg
precision	0.875220	0.883993	0.879556	0.879606	0.879606
recall	0.885333	0.873778	0.879556	0.879556	0.879556
f1-score	0.880247	0.878856	0.879556	0.879552	0.879552
support	1125.000000	1125.000000	0.879556	2250.000000	2250.000000

AIML Module Project - SUPERVISED LEARNING¶

Importing Required Python Modules and Libraries

Part ONE - Project Based

1. Import and Warehouse Data:

* Import all the given Datasets and Explore Shape and Size of each.

* Merge all Datasets onto One and Explore Final Shape and Size.

2. Data Cleansing:

* Explore and If Required Correct the Datatypes of Each Attribute.

* Explore for Null Values in the Attributes and If Required Drop or Impute values.

3. Data Analysis & Visualisation:

* Perform Detailed Statistical Analysis on the Data.

Brief Summary of Data¶

* Perform a detailed Univariate, Bivariate and Multivariate Analysis with Appropriate detailed comments after Each Analysis.

Univariate Analysis¶

Bivariate Analysis¶

Multivariate Analysis¶

4. Data Pre-Processing:

Outlier Analysis¶

Encoding Categorical Attribute¶

* Segregate Predictors VS Target Attributes

* Perform Normalisation or Scaling if Required.

* Check for Target Balancing. Add your comments.

Fixing Target Imbalance by Synthetic Minority Oversampling Technique (SMOTE)¶

* Perform Train-Test Split.

5. Model Training, Testing and Tuning:

* Design and Train a KNN Classifier.

* Display the Classification Accuracies for Train and Test Data.

* Display and Explain the Classification Report in detail.

Evaluating Performance of kNN Model¶

* Automate the Task of Finding Best Values of K for KNN.

Building KNN Model with K = 3 to Check Accuracies¶

* Apply all the possible Tuning Techniques to Train the best Model for the given data. Select the final best Trained Model with your comments for selecting this Model.

6. Conclusion and Improvisation:

* Write your Conclusion on the Results.

* Detailed suggestions or improvements or on quality, quantity, variety, velocity, veracity etc. on the data points collected by the research team to perform a better data analysis in future.

-------------------------------------------------- End of Part ONE -------------------------------------------------------

Part TWO - Project Based

1. Import and Warehouse Data:

* Import all the given Datasets and Explore Shape and Size of each.

* Merge all Datasets onto One and Explore final Shape and Size.

2. Data Cleansing:

* Explore and If Required Correct the Datatypes of each Attribute.

* Explore for Null Values in the Attributes and If Required Drop or Impute values.

Changing Datatype of LoanOnCard Attribute¶

3. Data Analysis & Visualisation:

* Perform Detailed Statistical Analysis on the Data.

Brief Summary of Data¶

Information about the Features¶

1. Qualitative Attributes:-¶

2. Quantitative Attributes:-¶

Dropping ID and ZipCode Attributes¶

Cleaning Negative Values of CustomerSince Attribute¶

Step 1 : Getting information about Negative Values¶

Step 2 : Performing suitable operation on Negative values¶

* Perform a detailed Univariate, Bivariate and Multivariate Analysis with Appropriate detailed comments after Each Analysis.

Univariate Analysis¶

Outliers in Each Discrete Attribute¶

Bivariate Analysis¶

Multivariate Analysis¶

Multivariate Analysis : To Check Relation Between Discrete Attributes¶

Multivariate Analysis : To check Density of Target Attribute in all other Discrete Features¶

Multivariate Analysis : To Check Correlation¶

4. Data Pre-Processing:

Outlier Analysis¶

Feature Scaling Standardization(Z-Score Normalization)¶

* Segregate Predictors VS Target Attributes

* Check for Target Balancing and Fix it if found Imbalanced.

Fixing Target Imbalance by Synthetic Minority Oversampling Technique (SMOTE)¶

* Perform Train-Test Split.

5. Model Training, Testing and Tuning:

* Design and Train a Logistic Regression and Naive Bayes Classifiers.

Logistic Regression¶

Naive Bayes Classifiers : Gaussian¶

* Display the Classification Accuracies for Train and Test Data.

* Display and Explain the Classification Report in detail.

Evaluating Performance of Logistic Regression and Naive Bayes Models¶

* Apply all the possible Tuning Techniques to Train the best Model for the given data. Select the final best Trained Model with your comments for selecting this Model.

6. Conclusion and Improvisation:

* Write your Conclusion on the Results.

* Detailed suggestions or improvements or on quality, quantity, variety, velocity, veracity etc. on the data points collected by the research team to perform a better data analysis in future.

	Age	CustomerSince	HighestSpend	HiddenScore	MonthlyAverageSpend	Level	Security	InternetBanking	LoanOnCard
9	34	9	180	1	8.9	3	0	0	1
10	65	39	105	4	2.4	3	0	0	0
11	29	5	45	3	0.1	2	0	1	0
12	48	23	114	2	3.8	3	1	0	0
13	59	32	40	4	2.5	2	0	1	0

	0	1	accuracy	macro avg	weighted avg
precision	0.857895	0.867568	0.862667	0.862731	0.862731
recall	0.869333	0.856000	0.862667	0.862667	0.862667
f1-score	0.863576	0.861745	0.862667	0.862661	0.862661
support	1125.000000	1125.000000	0.862667	2250.000000	2250.000000

	Trained Model	Accuracy (%)	Error Rate (%)
0	Logistic Regression	87.9	12.1
1	K-Neighbors Classifier	96.4	3.6
2	Gaussian Naive Bayes	86.6	13.4
3	RBF Support Vector Classifier	94.5	5.5
4	Linear Support Vector Classifier	88.1	11.9
5	Random Forest Classifier	98.7	1.3
6	Decision Tree Classifier	97.3	2.6